How to Make an AI: Step-by-Step Process, Use Cases, Technologies
Musk says it will kill us one day, yet we still use it with Google maps.
We are talking about artificial intelligence, the buzziest topic in recent decades. Since Deep Blue won the chess game against Gary Kasparov, businesses want to find out how to make an AI with a specific practical use.
Today, developing artificial intelligence is no longer rocket science. To accomplish this, you must find a task to solve with AI, collect data, select an algorithm, train said algorithm, and code an AI solution.
In this article, we'll discuss how to create an AI solution for your business and share the basic things you need to start, mainly:
- Key things to learn if you want to find out how to make your own AI: the definition, classification, history, and components of AI
- Main benefits of artificial intelligence computing and successful examples of AI implementation by businesses
- Step-by-step instructions for how to build an AI practically, whether you have to create an AI assistant or a complex prediction mechanism
Let’s get started!
What is AI?
Artificial intelligence is software that mimics human thinking and includes the ability to learn, solve problems, and make decisions.
No one knows how to make an AI that would replicate the natural human mind in all aspects.
However, the existing solutions have found many applications in different spheres of our lives. No wonder businesses are rushing to invest in AI.
According to the prognosis from Business Insights, the global artificial intelligence market will reach 1394.30 billion U.S. dollars by 2029 at a CAGR rate of 20.1% during the period between 2022 and 2029.
However, if you are joining the trend and are already thinking about how to make your own AI, you have to consider some facts.
To avoid some pitfalls, you should be prepared for a few things before you start the AI development process.
1. Expect a higher level of uncertainty
You may know how to program artificial intelligence, but you cannot foresee all the unknown factors AI projects usually have.
Building AI implies a high level of innovation, a high level of risk, and a high number of unexpected situations. Be ready for the fact that stakeholders may be unhappy if they feel that the technology underperforms.
So, it is essential to establish in the beginning that you can only count on specific conclusions made after your AI hypotheses have been tested.
2. Expect a higher level of complexity
Usually, more things are required to build an AI system than a usual project and the most significant difference is the technology used.
For an AI to make independent decisions, data collection, analytics, and pattern recognition are all required. For example, an autopilot car should collect data from sensors, microphones, cameras, radar, and the internet to make conclusions about driving. Conventional software only requires a few variables to work.
3. Be ready to hire a multi-disciplinary team
AI projects are diverse and finding people who know how to make an AI from A to Z is complex. Therefore, be ready to hire people from different backgrounds. These may include designers, engineers, data scientists, UX/UI designers, and Ph.D. data scientists.
Only the amalgamation of a wide variety of skills can guarantee a successful outcome.
4. Be ready for trials & failures
Want to know how to build an AI product to match your expectations?
Set the proper expectations. AI projects are not linear. There will be a lot of experimentation, versions, and testing before you even get preliminary results. Moreover, you will need to find out the exact results beforehand.
So, if you want to make your own AI, consider these points to avoid unexpected surprises.
Want to learn how to make an AI for your business but need help figuring out how to start?
Our software development team will consult you on everything you need to know to start your future AI project including the tech stack, cost, and development timeline.
AI vs. Machine Learning vs. Deep Learning
AI is tightly connected with both machine and deep learning, yet the latter two are components of AI.
Artificial intelligence is a general term that comprises different intelligence processes like learning, decision-making, and self-correction.
Machine learning is a subset of AI-related operations and refers to the ability of systems to learn from a given amount of data. Machine learning makes predictions based on labeled and structured data. For example, if an algorithm has to distinguish a house from a car in pictures, a human operator should input a set of features (roof, doors, etc.) that each category should have.
Deep learning is a method of machine learning. Unlike the latter, deep learning can learn from unstructured data like text or images, and extract some of their features. For example, if you need to do a task with cars and houses, deep learning can extract separate features like "doors" independently.
To build an independent AI system, you will be required to have all three components.
How Does AI Work?
To better understand how to program an AI, think about a simple math prediction like “I will buy pizza if I have money.” The prediction evolves into a complex solution by aggregating more variables like “if they put double cheese” or “if they offer a discount”, etc.
The more "ifs" that are included, the more complex an algorithm becomes.
A simple AI application will switch on the lights when you enter a room. A more complex mechanism will analyze your past interests by topic, shape, color, price, etc.
An app like Skyscanner, offering availability predictions, should analyze historical seasonal trends in customer behavior, which includes even more variables.
It is believed that with the aggregation of data and elaboration of algorithms, humanity will one day find out how to build an AI that pairs with the real human mind.
How to Create an AI: Types
Since the 60s, theories on how to create AI have been evolving continuously. Although the real-life applications of AI are still far from perfect, scientists predict that AI will be able to catch up with humans and even surpass them in the future.
Based on the level of elaboration, AI is split into three categories:
1. Narrow, or Artificial Narrow, Intelligence (ANI)
ANI comprises all the current AI applications from IBM's Deep Blue, which won a chess game with Garry Kasparov in 1997, to Apple's Siri, which is able to interpret human emotions.
2. General, or Artificial General, Intelligence (AGI)
There is no clear answer on how to create an AI of this category but it is predicted that one day it will be able to equal human intellect, including self-consciousness. We wouldn't have an answer if you were curious about how to make an AI like Jarvis from Avengers, but the concept presented in the movie is a perfect example of AGI.
3. Upper, or Artificial Super, Intelligence (ASI)
ASI is most often featured in the AI takeover scenarios that people like Elon Musk warn about. Although he actively employs ANI solutions in his Teslas, Musk thinks one day AI may be "our biggest existential threat."
Naveen Joshi from Forbes believes that once people learn how to build self-aware artificial intelligence, it will start to develop its own ideas of self-preservation. In other words, it could come into the Darwinian “struggle for existence” and perceive humans as a competing species. However, we are centuries away from this sort of technology.
Based on the likeness to the human mind, AI is divided into
- Reactive Machines
This type can mimic the human mind in reaction to different stimuli and is the mechanism that started the history of AI. Reactive machines do not learn; they just respond to inputs. The best example is IBM's Deep Blue, a unique-purpose chess-playing computer that was the first machine to defeat a human in a chess match.
- Limited Memory
This AI type can learn and make decisions. If you want to know how to make an AI that learns, you are thinking of a limited memory AI. All the present AI systems of this variety include software like Siri, Alexa, and navigation mechanisms.
- Theory of Mind
This type of AI only exists as a concept. These machines should be able to “understand” humans including such complexities as emotions, beliefs, and needs. Theory of mind AI will be perfect for creating an AI assistant. Emotion recognition machines, like the one enabled on Pepper Humanoid, are believed to be their predecessors.
In this stage, AI should develop self-awareness like the Avengers' Ultron after an Infinity Stone powered him. This is the ultimate goal of AI research although, for now, no one knows how to code an AI that will surpass humans in intellectual capabilities. Perhaps Apple’s Siri or Amazon’s Alexa are real-time predecessors to this technology.
By now, you've learned about the real-time possibilities of AI. In this next section, we'll discuss creating an AI solution in practice.
What is an AI Chatbot?
The chatbot is perhaps the simplest AI application in business with dozens of benefits. This solution refers to software that is able to imitate human communication.
You might be surprised, but the first person to learn how to make an AI chatbot was MIT professor Joseph Weizenbaum, who developed a chatting machine called Eliza in the 1960s.
Today, chatbots are used in almost all business branches to facilitate customer communication, 24/7 support, and instant request responses. Chatbots help delight and impress users, turn page engagement into leads, gain user data, and reduce operational costs by up to 30%.
How to Build an AI: Successful Examples
So, what should you know about how to make an AI in order for your business to grow?
Learn from examples.
PayPal, Facebook, and Uber have already implemented AI solutions. Check out how artificial intelligence transforms distinct industries today.
You are probably already used to personalized recommendations when you visit your favorite shopping sites. So, you are a profound AI user.
eCommerce websites train AI to learn your tastes and build personalized ads. For example, IBM developed an AI tool, Watson, that is able to build real-time proposals based on the customer’s current buying status. As well, companies make AI that learns user patterns to be able to identify fraudulent operations with credit cards and detect fake reviews.
Applications like Prezi or Evernote help students and teachers better organize learning materials and presentations.
For example, Evernote uses handwriting and speech recognition algorithms to transform handwritten or recorded material into text. Prezi allows users to merge presentations with a Zoom video session while using AI to bring real-time video and pre-prepared notes together.
We already use AI in different areas of our lives. For example:
- Automotive - autopilot cars like Tesla and Volvo use AI to enable their smart-driving mechanisms.
- Communication - Gmail uses AI to auto-filter spam messages and our cells use face recognition to verify our access.
- Fitness and wellness - fitness applications, like Fitness AI, have built-in AI trainers that operate similarly to human trainers. They can set up a workout routine, provide feedback on posture, and prompt the proper way to do an exercise.
- Home - many home appliances, like fridges, allow for voice control via Google Assistant or Alexa and other functions. For example, the LG InstaView refrigerator lets you monitor the contents of the fridge and order missing products via Amazon Alexa.
- Smart city - there is an AI solution that analyzes the current level of pollution in a certain area and makes predictions about how it will change in the next hours. This solution helps officials take public health and safety measures when necessary.
Uber and logistics companies use AI that allows them to automatically determine the number of cars on the road to predict a traffic jam and detect blockers (like construction) to create better routes for their drivers.
Not all robots are powered by AI, but recently, the most advanced ones rely heavily on artificial intelligence computing. A good example is Pepper, a machine designed to interact with people. Robotics powered by AI is most often used in stores to give personalized recommendations and help to find products, but can be used in other industries as well.
6. Human resources
If you want to know how to build your own AI for your HR department, check out the relevant examples. PayPal uses software called Talla to automate routine tasks and Netflix relies on Entleo to identify and approach passive talent. Other operations done with AI are candidate screening (Entleo, Harver), meeting scheduling (Bookafy), and onboarding (Talmundo).
If you want to know how to make an AI for finance, check out Underwrite.ai or Enova. These examples are powered by AI solutions that streamline different banking operations like lending facilitation, determination of loan eligibility, digital wealth management, detection of fraudulent operations, and more.
In marketing, AI is mostly used for automation and personalization.
Some companies focus on how to build an AI that is tuned to their specific needs. If you are one of them, you’ll have to hire a custom software development company to implement your ideas.
Others rely on ready-made solutions like Seventh Sense, which is a tool for behavioral analysis that prompts the optimal time to send a marketing email. Another solution, Phrasee, auto-generates natural-sounding headlines for your blogs and posts.
9. Social media
Mark Zuckerberg knows how to make an AI to generate billions since AI solutions make up the core of their business. Targeted content is the basic building block of Facebook, Twitter, or Instagram. They all use facial recognition extensively and have recently deployed a tool called DeepText, which is able to understand human conversations.
In the healthcare sector, AI systems are used for disease prediction. For example, the Viz.ai software analyzes patient data in order to alert doctors of negative tendencies and, thus, prevent diseases.
Buoy Health, developed with the assistance of a team out of Harvard Medical School, offers a tool that interacts with patients via a chatbot to help them select a proper consultant.
What Business Problems can be Solved with AI?
No one wonders whether or not AI was created for perfection. If you want to know how to make an AI to target your specific business needs, start with problems you can approach with AI solutions.
Consider the following:
Over 70% of businesses rely on AI to improve personalization efforts; the examples are various. The Symanto Insights Platform uses natural language processing to identify communication preferences, thus catering to personalization. The software can also help you determine if your customers are emotionally or rationally driven, allowing you to build a better communication strategy.
- Advanced search
Businesses widely use computer science AI search algorithms in domains like HR, science, e-commerce, etc. For example, Google's Semantic Scholar allows you to quickly search for the information you need from among 200 million academic papers. HR solutions like HireEZ or Seekout can screen over 600 million candidate profiles for the necessary skills and find potential candidates.
- Predicting user behavior
If you want to find specialists who know how to make an AI that predicts behavior, the easiest way is to search in e-commerce. 52% of e-commerce businesses use AI for different sorts of forecasting.
Based on the data about user demographics, billing, and purchase history, AI algorithms classify potential customers into different categories for the risk of churn. Other solutions allow users to predict future campaign results based on former strategies' results. This allows us to spend less money on people with no interest in particular products.
- Improved security level
With AI development, it is not only security, but also the threats become more elaborate. Therefore, businesses in different areas of industry like the military, healthcare, or even oil production, are bound to rely on software development services specializing in artificial intelligence computing.
The options vary if you are querying how to create an AI security solution. For example, an Israeli startup, UVeye, scans passing vehicles for bombs. Workers receive AI-based warnings on equipment failure in the oil and gas industry.
- Relevant ads
Advertising works if it is relevant and even lifesaving to customers. Usually, people don't like ads. For example, in the US, 32% do nothing when they see ads. These are the general cross-industry stats.
However, when it comes to traveling in unknown places, where timely advice can help escape unnecessary troubles, 72% of travelers say they would be more likely to visit a destination if it was advertised to them in a personalized way.
- User engagement
User engagement algorithms are about how to make an AI that caters to customers' different needs. Therefore, these tools differ in functionality.
For example, a service like Appcues offers personalized in-app onboarding based on user preferences, Fullstory allows customers to follow product updates or releases in real-time, and Twilio uses SMS, voice, and even video notifications to increase customer interest in specific products.
- Data mining
Data-driven business decisions stagger revenues. Therefore, the global big data analytics market, which was valued at over 240 billion US dollars in 2021, is projected to reach over 650 billion dollars by 2029.
If you are thinking about how to build a reliable AI product, you need to start with data mining. Data gives a more comprehensive picture of user behavior, and increases automation productivity.
- Fraud detection
Fraud detection AI gets integrated with payment processing systems widely. For example, Lyft partners with Anodot services, using AI algorithms to detect anomalous payments. Similar AI systems are used by Walmart, Stop & Shop, and Home Depot
- Object and facial recognition
This technology is widely used in the security arena and other industries. For example, smart cameras and sensors can prevent people from drunk driving by measuring moisture on the driver's hands.
- Discovery of data trends
If you want to learn how to build an AI to get to know your customers better, select AI algorithms that detect user behavior trends. These tools can help a business make predictions for selling strategies. For example, by analyzing past customer behavior, an AI tool can help you find the best time to send an email to each individual customer, thus improving deliverability and engagement.
- Computer vision
Computer vision relates to the machines that see or, in other words, can obtain information from images and videos and merge information from different cameras. This technology is widely used in medicine, as it allows the merging of different scan results into a 3D image and gives a better visual presentation of a medical problem.
- Speech recognition
Speech recognition is widely used in tons of apps. From Amazon's Alexa to language apps like Mondly and Babel, the applications of this technology are multiverse. If you are querying how to build an AI bot for messenger, an AI assistant, or an AI robot, you will start with speech recognition as a must-have feature.
How to Make an AI: Key Challenges One May Face
You cannot learn how to make an AI effectively if you don't consider the most common pitfalls that data-driven business encounter.
Here are some of the common challenges:
- Over-reliance on data and data scientists – you may be sure of the correctness of algorithms, but you cannot be 100% of the quality of input data.
- Lack of technical knowledge – typical tech errors may flaw the AI-driven results.
- Pre-coded technical bias – AI may inherit your bias. For example, Amazon stopped using its hiring mechanism because it favored candidates with the words like “executed” or “captured” in their resumes, which were primarily men.
- Low adaptation of BI tools – AI mechanisms change all the time and, to keep pace, the tools supporting them should evolve; ignoring them hinders progress.
In the next step, we shall discuss how to build an AI technically.
If you need support in formulating your business problem and avoiding common pitfalls with AI, contact MLSDev.
Our company will provide full support to establish the problem and outline the development process further.
Top AI Programming Languages
In this step of our query of how to code an AI, we shall discuss the programming languages used to build AI solutions.
Currently, Python is the most popular language for creating artificial intelligence and is well-supported. You may find tons of free materials online teaching how to make an AI in Python.
Other common languages are R, Java, Scala, and C++. Let’s review them one by one.
This is a perfect language if you want to know how to develop an AI with little experience. It is simple to read, learn, and use, even if you come from a marketing or business background. The abundance of online learning material makes it a go-to option for AI beginners.
This programming language is for data science and is a necessary branch of programming to train AI. Data science uses AI to find patterns in large data sets using mathematics. R is a perfect language to use to store, reprocess, transform and analyze large sets of data. Machine learning algorithms are usually written with R, so it’s a perfect choice if you are querying how to make an AI that learns.
This is the perfect language to run on any platform; therefore, it is used to build many solutions, including AI. Java benefits include open-source libraries which enable deep learning applications. As well, Java is perfect for big data processing, which is used in developing artificial intelligence.
This language is Java’s counterpart and was created to make Java more scalable, and both languages can work well together. Code written in Java is read in Scala and vice versa. Scala allows you to conduct many operations at the same time, which enables the function of computer-heavy operations.
This language is perfect to use in situations where computing power is low and makes it an ideal selection if you are interested in how to program an AI for IoT devices.
This is a language of future AI; it is gaining popularity now and is perfect for large projects performed by several teams.
How to Build an AI: Step-by-Step Process
Here's a detailed description of how to create your own AI. People questioning how hard it is to build an AI will be surprised: everything is approachable if you know what steps to take.
This is how to make your own AI application: identify a problem to solve, then assess your capabilities and set up a budget, collect and prepare the datasets, choose the algorithms in accordance with your problem, train them on the data sets you've prepared, and select the programming language.
Let's review them one by one.
1. Identify a problem or use case
So, you want to learn how to create an AI from scratch, but don’t know how to start. Begin with your business’ pain points.
Example: Uber had a hard time with traffic jams. So, they developed an analytical mechanism that collects real-time GPS data to select more accessible routes for Uber drivers. Before you decide to build an AI application for your business, invest the time necessary to detect your problem and think of a solution clearly.
2. Evaluate internal capabilities for tech adoption
The simplest facial recognition tool runs tons of math calculations in milliseconds, requiring huge computing power, updated software, and a powerful electric system.
Today’s software is preparing for the growth of AI operations. The latest Intel chips can do over 10 trillion calculations per second. However, these upgrades can be expensive.
The same refers to the team's readiness. Suppose you have employees who know how to program an AI that’s great. If not, you must establish a well-calculated budget to foresee future expenses in hiring external professionals and hardware upgrades.
3. Plan the team & development methodology
Like most contemporary projects, developing artificial intelligence requires agile methodology. However, the workflow is still different from conventional programming. In an AI project, the proof of concept has a bottom-up structure, meaning you start with implementation and then will see the result.
As well, most AI projects are related to high costs and extended deadlines. In this case, you need to have a fail-proof team that knows how to create an AI from scratch. If your internal team doesn’t have the capabilities required, hire a dedicated development team skilled in artificial intelligence computing. Usually, these teams consist of the following:
- Project manager and business analyst
- Data scientists and ML engineers
- Developers and QAs
4. Collect the correct raw data
In the next step, you have to collect and clean the data. You can use either structured data like excel tables with names, addresses, and phone numbers, or unstructured data like images and e-mails. When training your algorithms in each case, you must ensure that the data is clean.
Sometimes, you need to label the dataset with the help of the labeling team or people who know how to create an AI algorithm based on pre-prepared data sets. After you prepare the data, you must select an algorithm and the type of learning it will use.
5. Choose effective algorithms
The algorithm determines how your AI will proceed with the data. There are two types of AI algorithms: prediction and classification. It would help if you determined which will be better for your AI's task.
6. Select an AI platform
Luckily, today there are numerous ready-made tools and solutions to simplify artificial intelligence computing. Companies like Google, Microsoft, and Amazon provide services that help to create, train and deploy AI solutions.
Each platform has a selection of applications, study materials, and a community to discuss ideas.
7. Train your algorithms
Now, you must train algorithms based on the data collected. For algorithm accuracy, you’ll have to establish a minimum acceptable threshold to ensure you won't feed your AI false data.
For example, you need to know how to develop an AI that finds out the political tastes of the social media audience, but you know a large part of it is fraudulent. So, you can set a fraud score from 0 to 10 based on the set of features and train your algorithm to classify all the accounts as fake or real.
8. Select a suitable programming language
We've already discussed the existing programming languages applicable to AI and how each is best for a specific task. For example, beginners can easily learn how to build an AI in Python, R was developed for predictive analysis and statistics, C++ is great for AI games, and Java is perfect for large-scale projects.
9. Create an AI prototype, monitor & support it
AI is a system that is able to evolve, learn from errors, and develop all the time. So, once you create a prototype and deploy it, you will have to constantly monitor your AI machine to correct and regulate the processes in a timely manner so that the system doesn't take a wrong turn and put out erroneous results.
Future of Artificial Intelligence for Business
We hope that you now know how to create your own AI for your business. Let’s review what we’ve learned from this article:
- The AI sector is a growing field of the economy with business leaders competing to enhance their AI solutions. Uber, Google Maps, Apple's Siri, and Amazon's suggestion algorithms are all AI solutions that we can benefit from today; as well, they bring their businesses enormous fortunes.
- Although almost any programmer knows how to make an AI-based speech recognition app, it may take decades to find out how to make an AI like Jarvis. But even now, businesses using AI stand ahead of the competition with colossal personalization possibilities and data-driven solutions.
- Since the market demands growth, reliable companies are offering software development solutions that can help you develop artificial intelligence for your business.
- The primary condition for you to benefit from AI building is to find good guys who know how to build an AI successfully and pitch your idea to them.
Have a great idea for a stellar product and wish to make your own AI?
Contact MLSDev for a rough project estimation and advice! Our team would be delighted to polish your idea and will share everything with you about how to build your own AI solution successfully.